{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the high level transfer learning APIs, you can easily customize pretrained models for feature extraction or fine-tuning. \n",
    "\n",
    "In this notebook, we will use a pre-trained Inception_V1 model. But we will operate on the pre-trained model to freeze first few layers, replace the classifier on the top, then fine tune the whole model. And we use the fine-tuned model to solve the dogs-vs-cats classification problem,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Get the dogs-vs-cats datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the training dataset from https://www.kaggle.com/c/dogs-vs-cats and extract it. \n",
    "\n",
    "The following commands copy about 1100 images of cats and dogs into demo/cats and demo/dogs separately. \n",
    "```shell\n",
    "mkdir -p demo/dogs\n",
    "mkdir -p demo/cats\n",
    "cp train/cat.7* demo/cats\n",
    "cp train/dog.7* demo/dogs```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Get the pre-trained Inception-V1 model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the pre-trained Inception-V1 model from [Zoo](https://s3-ap-southeast-1.amazonaws.com/bigdl-models/imageclassification/imagenet/bigdl_inception-v1_imagenet_0.4.0.model) \n",
    " Alternatively, user may also download pre-trained caffe/Tensorflow/keras model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createDefault\n",
      "creating: createSGD\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "from bigdl.nn.criterion import CrossEntropyCriterion\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import DoubleType, StringType\n",
    "\n",
    "from zoo.common.nncontext import *\n",
    "from zoo.feature.image import *\n",
    "from zoo.pipeline.api.keras.layers import Dense, Input, Flatten\n",
    "from zoo.pipeline.api.keras.models import *\n",
    "from zoo.pipeline.api.net import *\n",
    "from zoo.pipeline.nnframes import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = init_nncontext(\"ImageTransferLearningExample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "manually set model_path and image_path for training\n",
    "\n",
    "1. model_path = path to the pre-trained models. (E.g. path/to/model/bigdl_inception-v1_imagenet_0.4.0.model)\n",
    "\n",
    "2. image_path = path to the folder of the training images. (E.g. path/to/data/dogs-vs-cats/demo/\\*/\\*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model_path = \"path/to/model/bigdl_inception-v1_imagenet_0.4.0.model\"\n",
    "image_path = \"file://path/to/data/dogs-vs-cats/demo/*/*\"\n",
    "imageDF = NNImageReader.readImages(image_path, sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----+\n",
      "|        name|label|\n",
      "+------------+-----+\n",
      "|cat.7361.jpg|  1.0|\n",
      "|cat.7451.jpg|  1.0|\n",
      "|cat.7646.jpg|  1.0|\n",
      "|cat.7197.jpg|  1.0|\n",
      "|cat.7011.jpg|  1.0|\n",
      "|cat.7830.jpg|  1.0|\n",
      "|cat.7771.jpg|  1.0|\n",
      "|cat.7618.jpg|  1.0|\n",
      "|cat.7580.jpg|  1.0|\n",
      "|cat.7549.jpg|  1.0|\n",
      "+------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "getName = udf(lambda row:\n",
    "                  re.search(r'(cat|dog)\\.([\\d]*)\\.jpg', row[0], re.IGNORECASE).group(0),\n",
    "                  StringType())\n",
    "getLabel = udf(lambda name: 1.0 if name.startswith('cat') else 2.0, DoubleType())\n",
    "\n",
    "labelDF = imageDF.withColumn(\"name\", getName(col(\"image\"))) \\\n",
    "        .withColumn(\"label\", getLabel(col('name')))\n",
    "(trainingDF, validationDF) = labelDF.randomSplit([0.9, 0.1])\n",
    "labelDF.select(\"name\",\"label\").show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune a pre-trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fine-tune a pre-trained model by removing the last few layers, freezing the first few layers, and adding some new layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createRowToImageFeature\n",
      "creating: createImageResize\n",
      "creating: createImageCenterCrop\n",
      "creating: createImageChannelNormalize\n",
      "creating: createImageMatToTensor\n",
      "creating: createImageFeatureToTensor\n",
      "creating: createChainedPreprocessing\n"
     ]
    }
   ],
   "source": [
    "transformer = ChainedPreprocessing(\n",
    "        [RowToImageFeature(), ImageResize(256, 256), ImageCenterCrop(224, 224),\n",
    "         ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageFeatureToTensor()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a pre-trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the Net API to load a pre-trained model, including models saved by Analytics Zoo, BigDL, Torch, Caffe and Tensorflow. Please refer to [Net API Guide](https://analytics-zoo.github.io/master/#APIGuide/PipelineAPI/net/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_model = Net.load_bigdl(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the last few layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print all the model layers and you can choose which layer(s) to remove.\n",
    "\n",
    "When a model is loaded using Net, we can use the newGraph(output) api to define a Model with the output specified by the parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\n",
      "conv1/7x7_s2\n",
      "conv1/relu_7x7\n",
      "pool1/3x3_s2\n",
      "pool1/norm1\n",
      "conv2/3x3_reduce\n",
      "conv2/relu_3x3_reduce\n",
      "conv2/3x3\n",
      "conv2/relu_3x3\n",
      "conv2/norm2\n",
      "pool2/3x3_s2\n",
      "inception_3a/3x3_reduce\n",
      "inception_3a/5x5_reduce\n",
      "inception_3a/relu_3x3_reduce\n",
      "inception_3a/relu_5x5_reduce\n",
      "inception_3a/pool\n",
      "inception_3a/1x1\n",
      "inception_3a/3x3\n",
      "inception_3a/5x5\n",
      "inception_3a/pool_proj\n",
      "inception_3a/relu_pool_proj\n",
      "inception_3a/relu_5x5\n",
      "inception_3a/relu_3x3\n",
      "inception_3a/relu_1x1\n",
      "inception_3a/output\n",
      "inception_3b/3x3_reduce\n",
      "inception_3b/5x5_reduce\n",
      "inception_3b/relu_3x3_reduce\n",
      "inception_3b/relu_5x5_reduce\n",
      "inception_3b/pool\n",
      "inception_3b/1x1\n",
      "inception_3b/3x3\n",
      "inception_3b/5x5\n",
      "inception_3b/pool_proj\n",
      "inception_3b/relu_pool_proj\n",
      "inception_3b/relu_5x5\n",
      "inception_3b/relu_3x3\n",
      "inception_3b/relu_1x1\n",
      "inception_3b/output\n",
      "pool3/3x3_s2\n",
      "inception_4a/3x3_reduce\n",
      "inception_4a/5x5_reduce\n",
      "inception_4a/relu_3x3_reduce\n",
      "inception_4a/relu_5x5_reduce\n",
      "inception_4a/pool\n",
      "inception_4a/1x1\n",
      "inception_4a/3x3\n",
      "inception_4a/5x5\n",
      "inception_4a/pool_proj\n",
      "inception_4a/relu_pool_proj\n",
      "inception_4a/relu_5x5\n",
      "inception_4a/relu_3x3\n",
      "inception_4a/relu_1x1\n",
      "inception_4a/output\n",
      "inception_4b/3x3_reduce\n",
      "inception_4b/5x5_reduce\n",
      "inception_4b/relu_3x3_reduce\n",
      "inception_4b/relu_5x5_reduce\n",
      "inception_4b/pool\n",
      "inception_4b/1x1\n",
      "inception_4b/3x3\n",
      "inception_4b/5x5\n",
      "inception_4b/pool_proj\n",
      "inception_4b/relu_pool_proj\n",
      "inception_4b/relu_5x5\n",
      "inception_4b/relu_3x3\n",
      "inception_4b/relu_1x1\n",
      "inception_4b/output\n",
      "inception_4c/3x3_reduce\n",
      "inception_4c/5x5_reduce\n",
      "inception_4c/relu_3x3_reduce\n",
      "inception_4c/relu_5x5_reduce\n",
      "inception_4c/pool\n",
      "inception_4c/1x1\n",
      "inception_4c/3x3\n",
      "inception_4c/5x5\n",
      "inception_4c/pool_proj\n",
      "inception_4c/relu_pool_proj\n",
      "inception_4c/relu_5x5\n",
      "inception_4c/relu_3x3\n",
      "inception_4c/relu_1x1\n",
      "inception_4c/output\n",
      "inception_4d/3x3_reduce\n",
      "inception_4d/5x5_reduce\n",
      "inception_4d/relu_3x3_reduce\n",
      "inception_4d/relu_5x5_reduce\n",
      "inception_4d/pool\n",
      "inception_4d/1x1\n",
      "inception_4d/3x3\n",
      "inception_4d/5x5\n",
      "inception_4d/pool_proj\n",
      "inception_4d/relu_pool_proj\n",
      "inception_4d/relu_5x5\n",
      "inception_4d/relu_3x3\n",
      "inception_4d/relu_1x1\n",
      "inception_4d/output\n",
      "inception_4e/3x3_reduce\n",
      "inception_4e/5x5_reduce\n",
      "inception_4e/relu_3x3_reduce\n",
      "inception_4e/relu_5x5_reduce\n",
      "inception_4e/pool\n",
      "inception_4e/1x1\n",
      "inception_4e/3x3\n",
      "inception_4e/5x5\n",
      "inception_4e/pool_proj\n",
      "inception_4e/relu_pool_proj\n",
      "inception_4e/relu_5x5\n",
      "inception_4e/relu_3x3\n",
      "inception_4e/relu_1x1\n",
      "inception_4e/output\n",
      "pool4/3x3_s2\n",
      "inception_5a/3x3_reduce\n",
      "inception_5a/5x5_reduce\n",
      "inception_5a/relu_3x3_reduce\n",
      "inception_5a/relu_5x5_reduce\n",
      "inception_5a/pool\n",
      "inception_5a/1x1\n",
      "inception_5a/3x3\n",
      "inception_5a/5x5\n",
      "inception_5a/pool_proj\n",
      "inception_5a/relu_pool_proj\n",
      "inception_5a/relu_5x5\n",
      "inception_5a/relu_3x3\n",
      "inception_5a/relu_1x1\n",
      "inception_5a/output\n",
      "inception_5b/3x3_reduce\n",
      "inception_5b/5x5_reduce\n",
      "inception_5b/relu_3x3_reduce\n",
      "inception_5b/relu_5x5_reduce\n",
      "inception_5b/pool\n",
      "inception_5b/1x1\n",
      "inception_5b/3x3\n",
      "inception_5b/5x5\n",
      "inception_5b/pool_proj\n",
      "inception_5b/relu_pool_proj\n",
      "inception_5b/relu_5x5\n",
      "inception_5b/relu_3x3\n",
      "inception_5b/relu_1x1\n",
      "inception_5b/output\n",
      "pool5/7x7_s1\n",
      "pool5/drop_7x7_s1\n",
      "Viewb5bcd097\n",
      "loss3/classifier\n",
      "prob\n"
     ]
    }
   ],
   "source": [
    "for layer in full_model.layers:\n",
    "    print (layer.name())\n",
    "model = full_model.new_graph([\"pool5/drop_7x7_s1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returning model's output layer is \"pool5/drop_7x7_s1\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Freeze some layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We freeze layers from input to pool4/3x3_s2 inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.freeze_up_to([\"pool4/3x3_s2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a few new layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createZooKerasInput\n",
      "creating: createZooKerasFlatten\n",
      "creating: createZooKerasDense\n",
      "creating: createZooKerasModel\n",
      "creating: createCrossEntropyCriterion\n",
      "creating: createScalarToTensor\n",
      "creating: createFeatureLabelPreprocessing\n",
      "creating: createNNClassifier\n"
     ]
    }
   ],
   "source": [
    "inputNode = Input(name=\"input\", shape=(3, 224, 224))\n",
    "inception = model.to_keras()(inputNode)\n",
    "flatten = Flatten()(inception)\n",
    "logits = Dense(2)(flatten)\n",
    "lrModel = Model(inputNode, logits)\n",
    "classifier = NNClassifier(lrModel, CrossEntropyCriterion(), transformer) \\\n",
    "        .setLearningRate(0.003).setBatchSize(40).setMaxEpoch(1).setFeaturesCol(\"image\") \\\n",
    "        .setCachingSample(False)\n",
    "pipeline = Pipeline(stages=[classifier])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The transfer learning can finish in a few minutes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createToTuple\n",
      "creating: createChainedPreprocessing\n",
      "creating: createTensorToSample\n",
      "creating: createChainedPreprocessing\n"
     ]
    }
   ],
   "source": [
    "catdogModel = pipeline.fit(trainingDF)\n",
    "predictionDF = catdogModel.transform(validationDF).cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----+----------+\n",
      "|        name|label|prediction|\n",
      "+------------+-----+----------+\n",
      "|dog.7002.jpg|  2.0|       2.0|\n",
      "|dog.7113.jpg|  2.0|       2.0|\n",
      "|dog.7016.jpg|  2.0|       2.0|\n",
      "|dog.7020.jpg|  2.0|       2.0|\n",
      "|dog.7030.jpg|  2.0|       2.0|\n",
      "|dog.7049.jpg|  2.0|       2.0|\n",
      "|dog.7053.jpg|  2.0|       2.0|\n",
      "|dog.7056.jpg|  2.0|       2.0|\n",
      "|dog.7058.jpg|  2.0|       2.0|\n",
      "|dog.7085.jpg|  2.0|       2.0|\n",
      "+------------+-----+----------+\n",
      "only showing top 10 rows\n",
      "\n",
      "+------------+-----+----------+\n",
      "|        name|label|prediction|\n",
      "+------------+-----+----------+\n",
      "|cat.7006.jpg|  1.0|       1.0|\n",
      "| cat.701.jpg|  1.0|       1.0|\n",
      "|cat.7012.jpg|  1.0|       1.0|\n",
      "|cat.7036.jpg|  1.0|       1.0|\n",
      "|cat.7060.jpg|  1.0|       1.0|\n",
      "|  cat.71.jpg|  1.0|       1.0|\n",
      "|cat.7118.jpg|  1.0|       1.0|\n",
      "| cat.713.jpg|  1.0|       1.0|\n",
      "|cat.7139.jpg|  1.0|       1.0|\n",
      "|cat.7142.jpg|  1.0|       1.0|\n",
      "+------------+-----+----------+\n",
      "only showing top 10 rows\n",
      "\n",
      "Test Error = 0.00813008 \n"
     ]
    }
   ],
   "source": [
    "predictionDF.select(\"name\",\"label\",\"prediction\").sort(\"label\", ascending=False).show(10)\n",
    "predictionDF.select(\"name\",\"label\",\"prediction\").show(10)\n",
    "correct = predictionDF.filter(\"label=prediction\").count()\n",
    "overall = predictionDF.count()\n",
    "accuracy = correct * 1.0 / overall\n",
    "print(\"Test Error = %g \" % (1.0 - accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the model from transfer learning can achieve over 95% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We randomly select some images to show, and print the prediction results here. \n",
    "\n",
    "cat: prediction = 1.0\n",
    "dog: prediction = 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "samplecat=predictionDF.filter(predictionDF.prediction==1.0).limit(3).collect()\n",
    "sampledog=predictionDF.filter(predictionDF.prediction==2.0).sort(\"label\", ascending=False).limit(3).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "for cat in samplecat:\n",
    "    print (\"prediction:\"), cat.prediction\n",
    "    display(Image(cat.image.origin[5:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 2.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 2.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 2.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dog in sampledog:\n",
    "    print (\"prediction:\"), dog.prediction\n",
    "    display(Image(dog.image.origin[5:]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
